To save trained model in Python, you can use various libraries and methods depending on your machine learning or deep learning framework. Below are general steps and examples for some popular libraries and frameworks like scikit-learn, TensorFlow, Pickle, joblib, and PyTorch.

Saving a Trained Model in scikit-learn:

import joblib

# Assuming you have a trained model named 'model'
model = ...

# Save the model to a file
joblib.dump(model, 'model_filename.pkl')

Saving a Trained Model in TensorFlow:

import tensorflow as tf

# Assuming you have a trained TensorFlow model
model = ...

# Save the model using TensorFlow's SavedModel format
tf.saved_model.save(model, 'saved_model_directory')

Saving a Trained Model in PyTorch:

import torch

# Assuming you have a trained PyTorch model
model = ...

# Save the model's state dictionary to a file
torch.save(model.state_dict(), 'model_state_dict.pth')

Using joblib

The more effective choice for serializing big NumPy arrays is joblib. When working with machine learning models that incorporate a lot of data, it is very helpful.

import joblib

# Suppose you have a trained model named 'model'
model = ...  # Your trained model object

# Save the model using joblib
joblib.dump(model, 'model.joblib')

Using Pickle

A common Python package for serializing and deserializing items is pickle. For huge arrays, it could be a little slower than joblib, but it still performs admirably in most situations.

import pickle

# Suppose you have a trained model named 'model'
model = ...  # Your trained model object

# Save the model using pickle
with open('model.pkl', 'wb') as file:
    pickle.dump(model, file)

Loading the Model:

To load the saved model, you can use the following code:

# Using joblib
loaded_model = joblib.load('model.joblib')

# Using pickle
with open('model.pkl', 'rb') as file:
    loaded_model = pickle.load(file)

These are basic examples of how to save models, and the exact method may vary depending on your specific use case and the model’s architecture. Here’s a breakdown of the steps:

1. Train your model: Firstly, train your machine learning or deep learning model using your data.
2. Choose a File Format: Then, decide on the file format in which you want to save your model. Common formats include pickle (.pkl) for scikit-learn models, TensorFlow’s SavedModel format for TensorFlow models, and PyTorch’s state_dict for PyTorch models.
3. Save the Model: Further, use the appropriate method to save your trained model to a file or directory. Make sure to provide a valid file name or directory path.
4. Loading the Model: Finally , in the future, when you want to use the trained model, you can load it using the corresponding loading method for your framework.

Remember to substitute the proper file directories where you wish to save and load your model for ‘model.joblib’ and ‘model.pkl’. Based on your unique requirements, select the serialization technique (pickle or joblib). Joblib could be a better option if you’re working with huge arrays because of its enhanced performance. However, remember to save any other relevant information alongside the model, such as pre-processing steps or hyperparameters, to ensure that you can reproduce the same results when you load the model later.